It is an interest in the field of camera manufacture to look at ways to make the operation of cameras more and more automatic. Cameras are already widely available that detect various settings for a subject at which a camera is pointed to provide better focus or exposure by measuring light levels and also by determining which part of a field of view should be made the subject of camera focus.
Cameras also exist that use learning mechanisms that respond to sensorial inputs, such as an image in an image field of the camera. “Unsupervised clustering of ambulatory audio and video” by Clarkson and Pentland (1998), Proceedings of the International Conference of Acoustics, Speech and Signal Processing, Phoenix, Ariz. 1999 (incorporated herein by reference) describes a camera with a method that learns to cluster situations, such as supermarket or office, based upon audio and video clues. The training of this system uses a Hidden Markov Model (HMM) that is trained by labeling situations manually.
A further example is provided in “Context Awareness by Analysed Accelerometer Data” (Randell & Muller. Editors MacIntyre & Ianucci, The Fourth International Symposium on Wearable Computers, pp 175-176, IEEE Computer Society, October 2002, incorporated herein by reference) which describes user input being used to train a clustering based situation classification system.
EP 1,109,132 (incorporated herein by reference) uses a user's opinion on the images presented, after they are captured, in a reinforcement learning framework to refine the ability of the system to predict what the user likes.